Waist‑height ratio and waist are
the best estimators of visceral fat
in type 1 diabetes
Erika B. Parente
1,2,3,4,7, Stefan Mutter
1,3,4,7, Valma Harjutsalo
1,3,4,5, Aila J. Ahola
1,3,4,
Carol Forsblom
1,3,4& Per‑Henrik Groop
1,3,4,6*Visceral fat is associated with cardiovascular and kidney disease. However, the relationship between body composition and anthropometric measures in type 1 diabetes is unknown. Using z‑statistics, we ranked the ability of body mass index (BMI), waist circumference (WC), waist‑hip ratio (WHR), waist‑height ratio (WHtR) and a body shape index (ABSI) to capture measures of body composition from 603 Dual‑energy‑X‑Ray‑Absorptiometry scans of adults with type 1 diabetes. Albuminuria was defined as urinary albumin excretion rate of at least 30 mg/24 h. Women with albuminuria had higher visceral fat mass % (VFM%) (0.9 vs. 0.5%, p = 0.0017) and lower appendicular lean mass % (AppLM%) (25.4 vs 26.4%, p = 0.03) than those without. Men with albuminuria had higher VFM% (1.5 vs. 1.0%, p = 0.0013) and lower AppLM% (30.0 vs 32.3, p < 0.0001) than those without. In men, WHtR estimated VFM% best (z‑statistics = 21.1), followed by WC (z = 19.6), BMI (z = 15.1), WHR (z = 14.6) and ABSI (z = 10.1). In women, the ranking was WC (z = 28.9), WHtR (z = 27.3), BMI (z = 20.5), WHR (z = 12.7) and ABSI (z = 10.5). Overall, the ranking was independent of albuminuria. Adults with type 1 diabetes and albuminuria have greater VFM% and lower AppLM% than those without. WHtR and WC best estimate the VFM% in this population, independently of albuminuria and sex.
Abbreviations
AFM Android fat mass
AFM% AFM/total body weight × 100
AppLM Appendicular-lean-mass (both legs and arms lean mass) AppLM% AppLM/total body-weight × 100
BFM Body fat mass
BFM% BFM/total body weight × 100 BLM Body lean mass
BLM% BLM/total body weight × 100 BMI Body mass index
DN Diabetic nephropathy
DXA Dual-energy X-ray Absorptiometry HDL High-density lipoprotein cholesterol VFM Visceral fat mass
VFM% VFM/total body weight × 100 WC Waist circumference WHR Waist-hip-ratio WHtR Waist-height ratio
The obesity epidemic is spreading worldwide. Along with this global trend, also people with type 1 diabetes have exhibited weight gain over the last years1. Such an increase in body weight will most likely have a harmful
OPEN
1Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, University of
Helsinki, Helsinki, Finland. 2Faculdade de Ciências Médicas da Santa Casa de São Paulo, São Paulo,
Brazil. 3Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki,
Helsinki, Finland. 4Abdominal Center, Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki,
Finland. 5National Institute for Health and Welfare, Helsinki, Finland. 6Department of Diabetes, Central Clinical
School, Monash University, Melbourne, Australia. 7These authors contributed equally: Erika B. Parente and Stefan
impact on the cardiovascular mortality rate of this population2,3. Previous research from our group showed that the mortality rate of individuals with type 1 diabetes increases starting from a body mass index (BMI) of 24.8 kg/m2, which is still within the normal range1. Therefore, it brings up the question of whether the BMI cut-off to define central obesity and cardiovascular risk in individuals with type 1 diabetes should be lower than the traditionally used 30 kg/m2.
BMI is the most commonly used tool to classify obesity, although it is an inadequate biomarker of abdomi-nal obesity4. It does not differentiate muscle from fat nor give the precise information regarding the amount of body fat mass, which better defines obesity5 and which is positively associated with cardiovascular disease (CVD)6,7. Furthermore, BMI neither distinguishes women from men concerning their body fat nor provides any information about the fat distribution, which is relevant for the risk of CVD8. Given that obesity is causally related to diabetic nephropathy (DN) in individuals with type 1 diabetes9, it would be of utmost importance to understand how the anthropometric measures are related to their body composition and especially visceral fat, which has been associated with dyslipidemia, insulin resistance, CVD8,10, and chronic kidney disease11,12 in the general population.
The Dual-energy X-ray Absorptiometry (DXA) is a well-recognized method to study fat distribution and body composition5. Unfortunately, the high cost hinders its use in routine clinical practice. On the other side, anthropometric measures such as waist circumference (WC) and waist-hip ratio (WHR) are accessible and of low cost, although they have certain limitations13. Another practical anthropometric measure is the waist-height ratio (WHtR) that has been associated with CVD in the general population and has the advantage to have a uni-sex cut-off value of 0.513–16, moreover, a meta-analysis has shown that WHtR is a better screening tool than WC and BMI for adult cardiometabolic risk factors16. A body shape index (ABSI) is another formula in which WC is adjusted for weight and height and has been a predictor of mortality, independently of BMI, in an American population from the National Health and Nutrition Examination Survey (NHANES)17.
Thus far, the relationship between these anthropometric measures and the body composition of adults with type 1 diabetes at different stages of DN, expressed as the presence or absence of albuminuria, has not previously been investigated. Therefore, this study aimed to explore these relationships and seek to identify the anthropo-metric measure to best estimate the visceral fat in this population with high cardiovascular risk.
Results
Data were available from a total of 246 men (30.5% with albuminuria) and 357 women (20.4% with albuminuria) (Table 1). In both men and women, individuals with albuminuria were older, had longer diabetes duration, higher systolic blood pressure and worse glycaemic control (Table 1).
Body composition according to the albuminuric stage.
In men, BMI was no different between indi-viduals with or without albuminuria (25.5 kg/m2 vs. 26.5 kg/m2, p = 0.08), albeit the two groups presented adif-ferent body composition. Men with albuminuria had greater BFM% (29.1% vs. 27.0%, p = 0.03), greater AFM% (3.0% vs. 2.4%, p = 0.02), and greater VFM% (1.5% vs. 1.0%, p = 0.001) compared with those without albuminu-ria (Table 1). On the other hand, men with albuminuria had lower BLM% (67.3% vs. 69.2%, p < 0.04) and lower AppLM% (30.0% vs. 32.3%, p < 0.0001) than the normoalbuminuric men (Table 1).
In women, BMI was greater in the albuminuric group (27.2 kg/m2 vs. 25.4 kg/m2, p = 0.04) than in the
nor-moalbuminuric group. The BFM% (36.9% vs. 35.6%, p = 0.39) and the AFM% (3.0% vs. 2.7%, p = 0.16) were no different between women with or without albuminuria. However, we observed greater VFM% (0.9% vs. 0.5%, p = 0.001) in women with albuminuria (Table 1). Regarding the muscle mass, women with albuminuria had lower AppLM% (25.4% vs. 26.4%, p = 0.02) despite no difference in the BLM% (59.6% vs. 61.0%, p = 0.29) compared to the normoalbuminuric women (Table 1).
Body composition according to sex.
In the whole cohort (independently of the presence or absence of albuminuria), men and women had comparable BMI (25.9 kg/m2 vs. 25.9 kg/m2, respectively, p = 0.87), despitedifferent body composition (Table 2). Men had lower BFM% (27.91% vs. 35.98%, p < 0.0001), greater BLM% (68.41% vs. 60.73%, p < 0.0001), and greater AppLM% (31.49% vs. 26.27%, p < 0.001) than women (Table 2). Although the AFM% was comparable between men and women (2.63% vs. 2.71%, p = 0.54), the VFM% was greater in men (1.20% vs. 0.58%, p < 0.0001).
Associations between body composition and anthropometric measures.
Measures ofBFM%. WHtR, followed by the WC, was the anthropometric measure to best estimate the BFM% in men,
independently of albuminuria (Table 3). In women, BMI followed by WC was the measure to best estimate the BFM%, also independently of albuminuria (Table 3). WHtR explained 60% of the BFM% variation in all men and 63% of the BFM% variation in all women. The BMI explained 47% of the BFM% variation in all men and 70% of the BFM% variation in all women (Table 3). In our linear model, the WHtR cut-off of 0.5 corresponded to a BFM% of 25.0 in all men and a BFM% of 34.6 in all women (Table 4).
Measures of VFM%. WHtR, followed by the WC, was the anthropometric measure to best estimate the VFM%
in men, while WC followed by WHtR was the best in women, independently of albuminuria (Fig. 1 and Table 3). The WHtR explained 68% of the VFM% variation in all men and 69% of the VFM% variation in all women (Table 3). WC explained 65% of the VFM% variation in all men and 71% of the VFM% variation in all women (Table 3). In our linear model, the WHtR cut-off of 0.5 corresponded to a VFM% of 1.1 in all men and a VFM% of 0.7 in all women (Table 4).
Measures of BLM%. WHtR, followed by WC, was the anthropometric measure to best estimate BLM% in all men, while BMI followed by WC was the best measure in women, independently of albuminuria (Table 3). The WHtR explained 59% of the BLM% variation in all men and 62% of the BLM% variation in all women (Table 3). BMI explained only 46% of the BLM% variation in all men but 69% of the BLM% variation in all women (Table 3).
Table 1. Body composition and clinical characteristics of men and women with and without albuminuria.
HDL High density lypoprotein, HbA1c glycated hemoglobin. Data shown in medians (interquartile ranges),
ABSI scaled A body shape index scaled by 100. P-Values were calculated separately for men and women by
permutation analysis with 10,000 permutations.
Men Women
Normoalbuminuria Albuminuria p-value Normoalbuminuria Albuminuria p-value
N 171 75 284 73
Age (years) 44.3 (37.6, 52.9) 52.5 (41.3, 58.8) 0.0005 40.2 (32.5, 50.5) 49.8 (42.5, 56.1) < 0.0001 Diabetes duration
(years) 22.4 (18.9, 34.7) 39.3 (30.3, 43.8) < 0.0001 24.0 (18.3, 34.7) 36.9 (24.8, 42.8) < 0.0001 Age of diabetes onset
(years) 18.5 (13.1, 25.8) 12.6 (7.5, 17.8) 0.0004 12.7 (8.3, 20.6) 12.2 (7.0, 19.2) 0.67 Systolic blood
pres-sure (mmHg) 138 (129, 148) 145 (133, 160) 0.01 125 (116, 136) 135 (125, 150) 0.0002 Diastolic blood
pres-sure (mmHg) 78 (72, 84) 76 (70, 82) 0.42 75 (69, 81) 77 (70, 84) 0.19 HbA1c (mmol/mol) 61 (54, 69) 66 (58, 74) 0.0072 61 (54, 70) 68 (59, 80) 0.006 HbA1c (%) 7.7 (7.1, 8.5) 8.2 (7.5, 8.9) 0.0056 7.7 (7.1, 8.6) 8.4 (7.5, 9.5) 0.0051 Total cholesterol (mmol/L) 4.49 (3.96, 5.08) 4.27 (3.60, 4.90) 0.20 4.47 (4.06, 5.03) 4.56 (3.92, 5.00) 0.34 HDL cholesterol (mmol/L) 1.42 (1.17, 1.75) 1.42 (1.17, 1.76) 0.96 1.63 (1.39, 1.91) 1.72 (1.39, 1.98) 0.15 Triglycerides (mmol/L) 1.00 (0.77, 1.38) 1.12 (0.87, 1.84) 0.17 0.82 (0.64, 1.11) 1.03 (0.75, 1.48) 0.0002 Body mass index
(kg/m2) 25.5 (24.0, 27.5) 26.5 (24.4, 29.0) 0.09 25.4 (22.7, 28.5) 27.2 (23.1, 31.0) 0.0477 ABSI scaled (m(11/6)/ kg(2/3)) 7.85 (7.55, 8.23) 8.21 (7.9, 8.56) < 0.0001 7.37 (7.06, 7.75) 7.62 (7.34, 8.06) 0.0019 Height (cm) 181 (175, 185) 178 (173, 184) 0.12 166 (161, 170) 164 (160, 167) 0.29 Body weight (kg) 83.74 (75.56, 93.62) 83.84 (77.62, 93.24) 0.95 70.63 (62.19, 79.93) 72.41 (61.86, 84.86) 0.54 Waist circumference (cm) 93 (85, 99) 98 (89, 107) 0.01 81 (75, 91) 88 (78, 99) 0.0059 Hip circumference (cm) 99 (94, 103) 101 (95, 106) 0.19 99 (92, 107) 101 (93, 110) 0.44 Waist-hip ratio 0.93 (0.89, 0.98) 0.96 (0.93, 1.03) 0.0047 0.83 (0.79, 0.87) 0.85 (0.82, 0.90) 0.0068 Waist-height ratio 0.50 (0.48, 0.55) 0.55 (0.50, 0.59) 0.0002 0.49 (0.45, 0.55) 0.54 (0.48, 0.60) 0.0007 Body fat mass (kg) 21.87 (16.43, 27.97) 24.82 (21.06, 30.42) 0.0431 25.10 (18.77, 32.93) 28.72 (19.32, 35.25) 0.0398 Body fat mass
per-centage (%) 27.0 (21.5, 31.5) 29.1 (25.9, 33.3) 0.0331 35.6 (30.0, 41.2) 36.9 (32.0, 44.4) 0.39 Android fat mass (kg) 2.05 (1.28, 2.99) 2.64 (1.72, 3.48) 0.0090 1.86 (1.15, 2.70) 2.24 (1.13, 3.54) 0.07 Android fat mass
percentage (%) 2.4 (1.6, 3.3) 3.0 (2.2, 3.9) 0.0295 2.7 (1.8, 3.5) 3.0 (1.9, 4.3) 0.16 Visceral fat mass (kg) 0.89 (0.46, 1.56) 1.30 (0.77, 2.05) 0.0070 0.39 (0.14, 0.80) 0.64 (0.26, 1.40) 0.0059 Visceral fat volume
(cm3) 939.31 (482.67, 1652.63) 1376.19 (820.60, 2175.97) 0.0044 410.84 (150.89, 843.25) 681.71 (276.14, 1480.26) 0.0078
Visceral fat mass
percentage (%) 1.0 (0.5, 1.7) 1.5 (0.9, 2.4) 0.0013 0.5 (0.2, 1.0) 0.9 (0.4, 1.7) 0.0017 Visceral-Android
fat ratio 0.45 (0.33, 0.56) 0.53 (0.43, 0.68) 0.0066 0.22 (0.12, 0.31) 0.30 (0.19, 0.45) 0.0005 Body lean mass (kg) 58.63 (53.90, 65.34) 56.31 (53.00, 61.30) 0.06 42.84 (39.75, 46.25) 42.72 (38.37, 48.90) 0.87 Body lean mass
percentage (%) 69.2 (65.0, 74.6) 67.3 (63.3, 70.4) 0.0436 61.0 (55.6, 66.1) 59.6 (52.8, 64.5) 0.29 Appendicular lean
mass (kg) 27.02 (24.21, 30.35) 25.20 (22.66, 28.24) 0.0083 18.74 (17.10, 20.36) 18.35 (16.35, 21.21) 0.43 Appendicular lean
The BLM% was negatively associated with the VFM% in all men (beta − 5.58 [− 6.17, − 4.98], r2 = 0.59, p = 7.09 × 10–48) and in all women (beta − 7.88 [− 8.65, − 7.11], r2 = 0.53, p = 1.23 × 10–60). The same association
pattern was seen for both the normoalbuminuric and the albuminuric stage.
The BFM%, VFM% and BLM% associations with anthropometric measures according to the albuminuric stages are shown in Table 3.
WHR and ABSI showed a low association with the BFM%, VFM% and BLM%, independently of the albu-minuric stage and sex (Table 3).
Misclassification of body fat.
Considering the normal threshold of BFM% below 25 for men and below 30 for women, the BMI misclassified 26.4% of the total cases (Table 5) by underestimating the body fat percent-age in 20.6% of the cases and overestimating it in 5.8% (Table 5). The misclassification of BFM% by WC, WHR and WHtR were 22.6% (Table S1), 37.1% (Table S2) and 26.6% (Table S3), respectively.Table 2. Body composition and clinical characteristics between men and women. HDL High density lypoprotein, HbA1c glycated hemoglobin, CKD Chronic kidney disease. Data shown in medians (interquartile ranges), ABSI scaled A body shape index scaled by 100. p-values were calculated by permutation analysis with 10,000 permutations.
Men Women p-value
N 246 357
Age (years) 46.62 (38.13, 55.8) 41.75 (33.26, 52.9) 0.0008 Diabetes duration (years) 26.56 (20.24, 39.53) 26.42 (19.39, 37.57) 0.96 Age of diabetes onset (years) 16.40 (10.90, 23.17) 12.48 (8.19, 20.19) < 0.0001 Systolic blood pressure (mmHg) 140.00 (129.00, 151.00) 127.00 (118.00, 140.00) < 0.0001 Diastolic blood pressure (mmHg) 77.50 (71.00, 84.00) 75.00 (69.00, 82.00) 0.0303 HbA1c (mmol/mol) 62.00 (55.00, 70.00) 63.00 (54.00, 72.00) 0.86 HbA1c (%) 7.82 (7.18, 8.56) 7.92 (7.09, 8.74) 0.86 Total cholesterol (mmol/L) 4.42 (3.89, 5.06) 4.48 (4.04, 5.02) 0.44 HDL cholesterol (mmol/L) 1.42 (1.17, 1.75) 1.63 (1.39, 1.92) < 0.0001 Triglycerides (mmol/L) 1.01 (0.78, 1.56) 0.84 (0.65, 1.21) < 0.0001 Body mass index (kg/m2) 25.9 (24.1, 28.4) 25.9 (22.8, 29.1) 0.87
ABSI scaled (m(11/6)/kg(2/3)) 7.99 (7.65, 8.35) 7.42 (7.13, 7.8) < 0.0001 Height (cm) 180.00 (174.85, 185.00) 165.50 (161.00, 170.00) < 0.0001 Body weight (kg) 83.79 (77.13, 93.62) 70.98 (61.86, 80.70) < 0.0001 Waist circumference (cm) 94.00 (86.00, 102.00) 82.00 (76.00, 93.00) < 0.0001 Hip circumference (cm) 100.00 (95.00, 104.00) 100.00 (93.00, 108.00) 1.00 Waist-hip ratio 0.94 (0.89, 1.00) 0.83 (0.79, 0.87) < 0.0001 Waist-height ratio 0.51 (0.48, 0.57) 0.5 (0.46, 0.56) 0.0384 Body fat mass (kg) 22.91 (17.40, 28.48) 25.39 (18.79, 33.44) 0.0228 Body fat mass percentage (%) 27.91 (22.69, 32.16) 35.98 (30.24, 41.61) < 0.0001 Android fat mass (kg) 2.23 (1.36, 3.10) 1.90 (1.14, 2.94) 0.0143 Android fat mass percentage (%) 2.63 (1.86, 3.45) 2.71 (1.87, 3.63) 0.54 Visceral fat mass (kg) 1.00 (0.51, 1.73) 0.41 (0.16, 0.87) < 0.0001 Visceral fat volume (cm3) 1063.96 (537.25, 1836.9) 438.67 (170.57, 926.56) < 0.0001
Visceral fat percentage (%) 1.20 (0.64, 1.93) 0.58 (0.26, 1.10) < 0.0001 Visceral-Android fat ratio 0.48 (0.37, 0.61) 0.23 (0.13, 0.33) < 0.0001 Body lean mass (kg) 57.9 (53.54, 64.52) 42.83 (39.66, 46.3) < 0.0001 Body lean mass percentage (%) 68.41 (64.19, 73.26) 60.73 (55.45, 66.04) < 0.0001 Appendicular lean mass (kg) 26.42 (23.64, 29.67) 18.62 (16.94, 20.44) < 0.0001 Appendicular lean mass percentage (%) 31.49 (29.41, 33.47) 26.27 (24.34, 28.43) < 0.0001
Normoalbuminuria (%) 69.51 79.55 0.0050 Microalbuminuria (%) 11.38 12.32 0.79 Macroalbuminuria (%) 19.11 8.12 0.0001 CKD stage 1 (%) 67.48 70.31 0.46 CKD stage 2 (%) 20.33 22.41 0.54 CKD stage 3 (%) 6.50 4.48 0.35 CKD stage 4 (%) 1.63 1.40 1.00 CKD stage 5 (%) 4.07 1.40 0.05
Dependent
variable Independent variable
MEN WOMEN
beta value p value r2 zeta value beta value p value r2 zeta value
ALL
BFM% BMI 1.27 [1.10, 1.45] 5.75E−35 0.47 14.5689 1.30 [1.21, 1.39] 2.62E−94 0.70 28.6701 BFM% WAIST 0.46[0.41, 0.50] 3.73E−47 0.58 18.1633 0.49 [0.45, 0.53] 2.19 E−79 0.63 24.7941 BFM% WHR scaled 5.84 [4.78, 6.89] 1.01E−22 0.33 10.8811 3.58 [2.39, 4.76] 6.79E−09 0.09 5.9407 BFM% WHtR scaled 8.53 [7.65, 9.41] 4.19E−50 0.60 19.0456 7.94 [7.29, 8.58] 2.31E−77 0.63 24.2819 BFM% ABSI scaled 4.49 [3.53, 5.46] 2.24E−17 0.26 9.1591 1.98 [1.01, 2.96] 7.53E−05 0.04 4.006 VFM% BMI 0.17 [0.15, 0.19] 1.23E−36 0.53 15.0568 0.10 [0.09, 0.11] 3.26E−62 0.56 20.5214 VFM% WAIST 0.06 [0.05, 0.06] 5.16E−52 0.65 19.6216 0.05 [0.04, 0.05] 4.98E−95 0.71 28.8623 VFM% WHR scaled 0.91 [0.79, 1.03] 3.91E−35 0.51 14.6141 0.58 [0.49, 0.67] 6.93E−31 0.34 12.7462 VFM% WHtR scaled 1.15 [1.05, 1.26] 9.52E−57 0.68 21.0667 0.73 [0.67, 0.78] 5.66E−89 0.69 27.2620 VFM% ABSI scaled 0.63 [0.50, 0.75] 3.04E−20 0.36 10.097 0.41 [0.33, 0.48] 9.06E−23 0.27 10.539 BLM% BMI − 1.20 [− 1.36, −1.03] 8.21E−34 0.46 − 14.2243 − 1.22 [− 1.31, −1.14] 1.11E−91 0.69 − 27.9460 BLM% WAIST − 0.43 [− 0.48, −0.38] 1.08E−45 0.57 − 17.7289 − 0.46 [− 0.50, −0.43] 3.01E−73 0.63 − 24.4874 BLM% WHR scaled − 5.48 [− 6.49, −4.47] 4.39E−22 0.33 − 10.6808 − 3.38 [− 4.50, −2.26] 7.87E−08 0.09 − 5.9310 BLM% WHtR scaled − 8.02 [− 8.87, −7.17] 2.49E−48 0.59 − 18.5140 − 7.47 [− 8.09, −6.86] 1.10E−69 0.62 − 23.9328 BLM% ABSI scaled − 4.24 [− 5.16, −3.32] 4.47E−17 0.26 − 9.0586 − 1.91 [− 2.83, −0.99] 5.52E−05 0.05 − 4.0823 NORMOALBUMINURIA
BFM% BMI 1.29 [1.07, 1.51] 2.75E−23 0.45 11.6211 1.33 [1.22, 1.44] 3.62E−72 0.68 24.6341 BFM% WAIST 0.47 [0.40, 0.53] 1.37E−31 0.56 14.5603 0.51 [0.47, 0.56] 2.79E−59 0.61 20.9316 BFM% WHR scaled 5.50 [4.15, 6.86] 1.74E−13 0.28 8.0184 2.90 [1.51, 4.28] 5.11E−05 0.06 4.1141 BFM% WHtR scaled 9.01 [7.85, 10.17] 7.53E−34 0.59 15.3684 8.18 [7.40, 8.97] 7.16E−58 0.60 20.5365 BFM% ABSI scaled 4.25 [3.09, 5.41] 1.73E−11 0.24 7.219 1.38 [0.33, 2.43] 9.96E−03 0.02 2.5948 VFM% BMI 0.15 [0.13, 0.18] 1.55E−24 0.49 12.0640 0.09 [0.08, 0.10] 4.86E−49 0.55 18.0909 VFM% WAIST 0.06 [0.05, 0.06] 1.28E−36 0.63 16.3685 0.04 [0.04, 0.04] 8.21E−73 0.69 24.8252 VFM% WHR scaled 0.83 [0.69, 0.96] 4.22E−24 0.49 11.9099 0.44 [0.34, 0.53] 1.31E−17 0.25 9.1377 VFM% WHtR scaled 1.11 [0.99, 1.23] 1.62E−40 0.67 17.8027 0.65 [0.60, 0.70] 1.21E−69 0.68 23.8945 VFM% ABSI scaled 0.56 [0.43, 0.69] 8.10E−15 0.34 8.5327 0.30 [0.22, 0.37] 1.61E−14 0.21 8.1069 BLM% BMI − 1.21 [− 1.342 −1.00] 2.31E−22 0.44 − 11.2931 − 1.25 [− 1.35, −1.15] 9.06E−70 0.67 − 23.9314 BLM% WAIST − 0.44 [− 0.50, −0.38] 1.80E−30 0.55 − 14.1632 − 0.49 [− 0.53, −0.44] 2.18E−58 0.60 − 20.6812 BLM% WHR scaled − 5.17 [− 6.46, −3.87] 4.19E−13 0.27 − 7.8683 − 2.73 [− 4.05, −1.42] 5.68E−05 0.06 − 4.0881 BLM% WHtR scaled − 8.47 [− 9.59, −7.35] 1.32E−32 0.57 − 14.9230 − 7.71 [− 8.46, −6.96] 1.15E−56 0.59 − 20.1996 BLM% ABSI scaled − 4.02 [− 5.13, −2.91] 2.23E−11 0.24 − 7.1732 − 1.36 [− 2.35, −0.36] 7.62E−03 0.03 − 2.6878 ALBUMINURIA
BFM% BMI 1.19 [0.91, 1.46] 7.13E−13 0.51 8.7138 1.23 [1.06, 1.40] 1.06E−22 0.75 14.4528 BFM% WAIST 0.42 [0.34, 0.50] 9.42E−16 0.59 10.2710 0.45 [0.39, 0.52] 1.17E−21 0.73 13.8017 BFM% WHR scaled 6.42 [4.59, 8.25] 1.21E−09 0.40 6.9794 5.76 [3.31, 8.21] 1.32E−05 0.24 4.6882 BFM% WHtR scaled 7.60 [6.17, 9.03] 2.36E−16 0.61 10.6015 7.62 [6.49, 8.75] 4.90E−21 0.72 13.4210 BFM% ABSI scaled 4.23 [2.44, 6.01] 1.14E−05 0.24 4.7174 3.93 [1.74, 6.13] 6.41E−04 0.16 3.5746 VFM% BMI 0.19 [0.15, 0.23] 9.37E−14 0.60 9.1872 0.13 [0.10, 0.15] 3.69E−15 0.60 10.0194 VFM% WAIST 0.07 [0.05, 0.08] 7.43E−16 0.65 10.3277 0.05 [0.05, 0.06] 2.46E−22 0.75 14.2240 VFM% WHR scaled 1.07 [0.80, 1.34] 2.01E−11 0.54 7.9376 0.97 [0.74, 1.20] 2.49E−12 0.52 8.4698 VFM% WHtR scaled 1.19 [0.97, 1.41] 7.48E−17 0.67 10.8776 0.87 [0.73, 1.01] 5.88E−20 0.71 12.7699 VFM% ABSI scaled 0.63 [0.35, 0.91] 2.74E−05 0.32 4.4807 0.72 [0.50, 0.94] 6.42E−09 0.40 6.608 BLM% BMI − 1.12 [− 1.38, −0.86] 1.23E−12 0.51 − 8.5875 − 1.15 [− 1.31, −0.99] 1.75E−22 0.75 − 14.3175 BLM% WAIST − 0.40 [− 0.48, −0.32] 2.09E−15 0.59 − 10.0821 − 0.42 [− 0.48, −0.36] 2.06E−21 0.73 − 13.6507 BLM% WHR scaled − 6.03 [− 7.79, −4.27] 2.18E−09 0.39 − 6.8395 − 5.45 [− 7.76, −3.15] 1.15E−05 0.25 − 4.7251 Continued
Dependent
variable Independent variable
MEN WOMEN
beta value p value r2 zeta value beta value p value r2 zeta value
BLM% WHtR scaled − 7.15 [− 8.53, −5.77] 8.14E−16 0.60 − 10.3058 − 7.16 [− 8.23, −6.09] 6.63E−21 0.72 − 13.3407 BLM% ABSI scaled − 3.94 [− 5.65, −2.23] 1.81E−05 0.23 − 4.593 − 3.68 [− 5.75, −1.61] 6.91E−04 0.16 − 3.5513 Table 3. Associations between body composition and anthropometric measures according to the albuminuric stages. BMI body mass index (kg/m2), WAIST waist circumference (cm), WHR scaled waist-hip ratio*10,
WHtR scaled waist-height ratio*10, ABSI scaled a body shape index scaled by standard deviation, BFM %
body fat mass percentage, BLM % body lean mass percentage, VFM % visceral fat mass percentage, Beta linear regression coefficients adjusted for age, r2 coefficient of determination from the linear regression, the relevance
ranking of each variable was based on the z statistics.
Table 4. Percentage of total and visceral body fat related to BMI, WC and WHtR cut-offs according to the albuminuric stages. BMI body mass index (kg/m2), WC waist circumference (cm), WHtR waist-height ratio, BFM% body fat mass percentage, VFM% visceral fat mass percentage. WC was considered normal if < 94 cm for men and < 80 cm for women. WHtR was considered normal if < 0.5 for both sexes. BMI was considered normal if < 25 kg/m2, overweight if ≥ 25 and < 30 kg/m2 and obese if ≥ 30 kg/m2.
Sex BMI 25 BMI 30 WC (94/80) WHtR 0.5 BFM% All Men 25.6 31.8 26.9 25.0 Women 33.9 40.4 33.3 34.6 Normoalbuminuria Men 25.0 31.2 26.7 24.9 Women 34.0 40.6 33.4 34.9 Albuminuria Men 27.2 33.0 27.4 25.4 Women 33.9 40.0 32.7 33.5 VFM% All Men 1.2 2.0 1.3 1.1 Women 0.6 1.1 0.5 0.7 Normoalbuminuria Men 1.1 1.8 1.3 1.0 Women 0.6 1.0 0.5 0.6 Albuminuria Men 1.5 2.3 1.5 1.1 Women 0.8 1.5 0.6 0.7
Figure 1. Associations between visceral fat and anthropometric measures in adults with type 1 diabetes according to sex.
Discussion
In this study, we investigated for the first time the body composition of individuals with type 1 diabetes with and without albuminuria. We showed that individuals with albuminuria, regardless of sex, had greater visceral fat percentage and lower appendicular lean mass percentage, which is a dangerous combination regarding the risk of CVD8,18,19. This finding is even more relevant if we consider that this population already has a high car-diovascular risk because of diabetes and DN1,3,20.
We showed that individuals with similar BMI have very different body composition, especially concerning the percentage of visceral fat and lean mass. This result emphasizes the importance of knowing the body composition instead of only BMI in a high cardiovascular risk population. The increase in body weight with a central distribu-tion favours the accumuladistribu-tion of visceral fat that leads to an inflammatory state and insulin resistance10, which has been associated with kidney disease11,12,21. To make the situation worse, low lean mass22 and kidney disease23 are also associated with muscle insulin resistance. Considering that this is a cross-sectional study, it is not pos-sible to say if the visceral fat, which is linked to chronic inflammation and insulin resistance, is contributing to the development of albuminuria or the albuminuric stage is worsening the visceral fat and insulin resistance due to physical inactivity, inflammation, changes in the microbiome or other factors21.
Most importantly, in this study, we found a strong association between VFM% and simple measures such as WHtR and WC, independently of the albuminuric stage and sex. We are not aware of any other study in individuals with type 1 diabetes that have previously assessed such relationships, especially looking at different stages of albuminuria. WHtR has been associated with central obesity and cardiovascular risk in the general population and in people with type 2 diabetes13–15. Furthermore, in a large prospective study including 109,536 postmenopausal women, it has been linked to cardiovascular events24. However, the relationship between WHtR and VFM% has never been described in individuals with type 1 diabetes with and without albuminuria. Given that DN increases cardiovascular mortality several-fold20,25 and that visceral fat is closely associated with CVD8,
18, our results regarding the association between WHtR and visceral fat are consistent with the literature which has shown the WHtR is a better screening tool than BMI for cardiometabolic risk factors16. Therefore, this study brings up new important information regarding central obesity in individuals with type 1 diabetes, a subject that most of the time has been related to type 2 diabetes. In this respect, it is important to acknowledge that obesity is increasing among individuals with type 1 diabetes and at the same time, there has been an increase in the mortality rate starting from a normal range of BMI1.
Another novel finding of this study was the negative association between BLM% and WHtR and WC, inde-pendently of the albuminuric stage. Although BLM% was best estimated by WHtR in men and by BMI in women, the simple measurement of WC was the second-best for the estimation of BLM% in both sexes. A plausible expla-nation for why WC and WHtR can estimate the percentage of body lean mass is the negative association between BLM% and VFM%. Although a recent publication showed that the fat-free mass was not associated with CVD7, it does not exclude the relevance of our findings, since one has to take into consideration that the fat-free mass measured by bioimpedance includes not only the muscle mass and, in our study, we measured the body lean mass by DXA, which has better accuracy than the bioimpedance5. From a clinical perspective, we found a simple and accessible tool to estimate the body lean mass in individuals with type 1 diabetes, independently of albuminuria.
Since low skeletal muscle mass is linked to CVD19 and muscle wasting has been associated with premature death in individuals with end-stage renal disease26 another important clinical finding was that, independently of sex, individuals with albuminuria have lower AppLM% compared to those without albuminuria, which might contribute to the increase in the cardiovascular risk of this high-risk population. Muscle wasting is not rare in individuals with end-stage renal disease26,27 and although we did not include such individuals in our analyses, we showed that individuals with type 1 diabetes at the earlier stages of DN (micro and macroalbuminuria) already show a decrease in their BLM% compared to those with normoalbuminuria.
According to previous publications including individuals with obesity and/or type 2 diabetes, in the current study, the ABSI was positively associated with central obesity and negatively associated with body lean mass28,29. However, it was inferior to the other anthropometric measures for the estimation of BFM%, VFM% and BLM% in our sample composed by Caucasian-Finnish individuals with type 1 diabetes. Since ABSI is a formula composed by WC adjusted for weight and height, the association between ABSI and body composition may vary depending on the characteristics of the studied population and on ethnicity.
In our study, BMI was not the anthropometric measure to best estimate VFM% in both sexes, and this inability of BMI to reflect the abdominal fat has been discussed erlier4. It was not useful to estimate BFM% and BLM% in men either, although it was in women. The relationship between BMI and BFM% was studied previously in the general population30 and the percentage of body fat mass related to the BMI was similar to our study. However, Table 5. Misclassification of body fat percentage by BMI. The normal body fat percentage was considered: ≤ 25 for men and ≤ 30 for women. DXA: Dual-energy X-Ray Absorptiometry. Percentages are based on the total number of 603 scans.
BMI < 25
(n) BMI ≥ 25(n) TOTAL(n)
Normal body fat percentage (n, %) 137 (22.7) 35 (5.8) 172 (28.5) Excess body fat percentage (n, %) 124 (20.6) 307 (50.9) 431 (71.5)
the American study30 did not investigate the associations between VFM%, BLM% with BMI, not either with WHtR and WC such as our study.
Furthermore, BMI misclassified BFM% in 26% of the total cases and underestimated it in 21% of them. Although the level of misclassification by BMI in our study is lower than in a previous study31, it might be explained by the different methods used to assess the body composition. In the previous study, they used bioim-pedance while we used DXA, which provides better accuracy5. However, such as misclassification is clinically relevant, since individuals considered to have normal body weight by BMI might, in fact, have an excess of body fat and visceral fat, which are both closely associated with cardiovascular mortality8,13,32,33. The misclassification by BMI is another possible explanation of why the mortality rate in individuals with type 1 diabetes starts to increase already from the normal range of BMI1 Interestingly, the WC and WHtR misclassify the BFM% at least similarly to BMI, although they are in fact measures to estimate the central fat and not the total body fat. This finding is clinically important since a simple measure of WHtR or WC could not only better estimate visceral fat than BMI, but was able to classify obesity (BFM%) as well as BMI.
Another novelty of this study is to show, by our linear models, how much of the body fat mass and visceral fat mass percentages are related to the cut-offs of BMI, WC and WHtR. Interestingly, the BFM% and VFM% related to the BMI of 25 kg/m2 are similar to BFM% and VFM% related to the WHtR of 0.5. Therefore, our finding may
provide a clue, why there is an increase in the mortality rate of this population starting from a BMI of 24.8 kg/ m21. These results may question whether the BMI of 25 kg/m2 is the best cut-off to define central obesity and
cardiovascular risk in individuals with type 1 diabetes.
A limitation of this study is that we can not exclude confounding factors such as lifestyle and ethnicity. Since we studied a homogenous all-Caucasian Finnish population with type 1 diabetes and there are different thresh-olds for waist circumference and BMI for different ethnicities4,34, our results may not be applicable for all ethnici-ties. Another limitation is its cross-sectional design; therefore, it is not possible to conclude any causality between the associations we found nor any prediction of CVD risk. However, these results motivate further prospective studies to investigate the impact of body composition on chronic diabetes complications in individuals with type 1 diabetes. Another strength is its wide applicability to clinical practice since we here provide easily applicable tools to estimate the percentage of visceral fat and lean mass in a population with a high cardiovascular risk.
In conclusion, this study shows that simple measures such as WHtR and WC can estimate the VFM% in adults with type 1 diabetes independently of albuminuric stage and sex. Furthermore, it showed that individuals with type 1 diabetes and albuminuria, a population of high CVD risk, have greater VFM% and lower AppLM% compared to those with normoalbuminuria. From the clinical perspective, this study supports the routine moni-toring of WHtR in adults with type 1 diabetes.
Methods
Study design and population.
In this cross-sectional study, we explored the body composition and its relationship with anthropometric measures in individuals with type 1 diabetes with and without albuminuria. Adults with type 1 diabetes (n = 579) from the Finnish Diabetic Nephropathy (FinnDiane) Study cohort that had a DXA scan (n = 603) between August 2011 and June 2019 were included in this analysis. For 24 individuals, two scans were performed 5.3 ± 1.4 years apart as part of a regular follow-up visit. Sensitivity analysis including only the first scan (n = 579) for each individual led to the same conclusions (data not shown). Since 1997 the FinnDiane Study is recruiting and thoroughly characterizing individuals with type 1 diabetes 18 years or older at 93 centers across Finland. Type 1 diabetes is defined as age at onset of diabetes under 40 years and permanent insulin treatment initiated within a year from the diabetes diagnosis. The study protocol is designed according to the principles of the Declaration of Helsinki as revised in 2000 and was approved by the Ethics Committee of Helsinki and Uusimaa Hospital District. Written informed consents are obtained from each FinnDiane Study participant.Diabetic nephropathy (DN) stage.
Normoalbuminuria was defined as an urinary albumin excretion rate (UAER) < 20 µg/min or < 30 mg/24 h in at least two out of three urine samples. Microalbuminuria was defined as a UAER ≥ 20 and < 200 µg/min or ≥ 30 and < 300 mg/24 h and macroalbuminuria as a UAER ≥ 200 µg/ min or ≥ 300 mg/24 h. Individuals with micro- or macroalbuminuria were pooled together for analyses and will be referred throughout the paper as the albuminuric group. Individuals with end-stage renal disease (either on dialysis or have received a kidney transplant) were not included in this study.Body composition and anthropometric measurements.
Body composition was evaluated by DXA (GE Healthcare Lunar version 16, Wisconsin, USA) according to the manufacturer’s instructions and visceral fat was measured by CoreScan35. A total body fat percentage below 25 was considered normal for men and below 30 for women30,31. The body fat mass percentage (BFM%), android fat mass percentage (AFM%), visceral fat mass percentage (VFM%), body lean mass percentage (BLM%), and appendicular lean mass percentage (AppLM%) were calculated by dividing the variable (grams) by total body weight (grams). Appendicular lean mass refers to the lean mass of both legs and arms.BMI was calculated as total body weight (kilograms) divided by the square of the height (meters)36 and indi-viduals were stratified by their BMI as normal weight (19–24.9 kg/m2), overweight (25–29.9 kg/m2) and obese
(≥ 30 kg/m2)36. WC was measured in centimeters by a stretch‐resistant tape at the horizontal plane midway in the distance of superior iliac crest and the lower margin of the last rib34. The WC was considered normal if it was below 94 cm for men and below 80 cm for women37. The hip circumference was measured with the same tape around the widest part over the great trochanters and WHR was calculated by dividing the WC by the hip circumference34. A WHR below 0.9 for men and below 0.85 for women were considered normal34. The WHtR
was calculated by dividing the WC by the height and values below 0.5 were considered normal for both men and women13. ABSI was calculated by the formula WC/(BMI2/3height½)17.
Statistical analyses.
For the analyses, we split the cohort into men and women and further distinguished between individuals with and without albuminuria.Data on categorical variables are presented as frequencies, while continuous variables as medians (interquar-tile ranges, IQR). Between-group comparisons were conducted via permutation tests with 10,000 permutations. To compare the anthropometric measures for their capacity to estimate body composition, we performed linear regression analyses adjusted for age with the measurements of body composition as the dependent variables and anthropometric measures as the independent variables. The regressions’ R2 values were used to describe the
proportion of the variance of a body composition measure that is explained by an anthropometric measure. The relevance ranking of each variable was based on the z statistics allowing comparisons across risk factors independently of their measurement units38. WHR and WHtR were scaled by a factor of 10. All analyses were performed in R39.
Received: 18 May 2020; Accepted: 1 October 2020
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Acknowledgements
The authors would like to acknowledge all physicians and nurses at each FinnDiane center participating in patient recruitment and characterization.
Author contributions
E.B.P. was responsible for the study design. E.B.P. and S.M. contributed equally to the manuscript prepara-tion (shared first authors). S.M. and V.H. were responsible for the statistical analysis. P.H.G., E.B.P., S.M., V.H. interpreted the results and contributed to the critical revision of the manuscript. C.F. and A.J.A. contributed to acquisition of the data and critical revision of the manuscript. P.H.G. is the guarantor of this work and takes full responsibility for the integrity of the data and the accuracy of the data analysis. All authors reviewed the manuscript and approved the final version.
Funding
This research was funded by grants from the Folkhälsan Research Foundation, Academy of Finland (316664 and 275614), Wilhelm and Else Stockmann Foundation, Liv och Hälsa Society, Novo Nordisk Foundation (NNF OC0013659) and The Diabetes Research Foundation.
Competing interests
EBP reports receiving lecture honorariums from Eli Lilly, Abbott, Astra Zeneca, Sanofi, Boehringer Ingelheim and is an advisory board member of Sanofi. P-.H.G. reports receiving lecture honorariums from Astellas, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Medscape, MSD, Mundipharma, Novo Nordisk, PeerVoice, Sanofi, SCI-ARC and being an advisory board member of Astellas, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Medscape, MSD, Mundipharma, Novo Nordisk, and Sanofi. SM, VH, AJA and CF report no conflict of interest.
Additional information
Supplementary information is available for this paper at https ://doi.org/10.1038/s4159 8-020-75667 -5. Correspondence and requests for materials should be addressed to P.-H.G.
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